Following the work of [1]}, one can obtain a shallow trig network representation of GP regression model with SE kernel if the features \(\Omega \)  are sampled from a normal distribution. Similarly, the GP regression models with Laplacian and Cauchy kernels can have their network representation if the features are sampled from different single-mode distributions. The following lemma show that the model with spectra mixture kernel is equivalent to the shallow trig network if the features are sampled from a mixture of Gaussians.
